Atmospheric aerosols are a key factor influencing the Earth's radiation balance and climate change, and the accuracy of their retrieval is crucial for environmental monitoring and climate research. FY-4A AGRI, with its high-frequency observation capability, can provide aerosol data at high temporal resolution. Combined with deep learning technology, it enables efficient monitoring of dynamic aerosol variations. This study develops a retrieval algorithm for aerosol optical and microphysical parameters based on the Transformer deep learning model, specifically designed for the FY-4A geostationary satellite. The algorithm achieves multi-parameter collaborative retrieval of aerosol optical depth (AOD), fine/coarse-mode aerosol optical depth (FAOD/CAOD), and single scattering albedo (SSA). This research overcomes the reliance on prior assumptions inherent in traditional physical retrieval methods. By integrating multi-band spectral features, geometric observation parameters, and data from 104 AERONET sites, it significantly enhances retrieval accuracy under the complex surface conditions of East Asia. Experimental results demonstrate high accuracy in validation against AERONET sites, with correlation coefficients of R=0.915 for AOD, R=0.897 for FAOD, R=0.851 for CAOD, and R=0.536 for SSA. Comparative validation of various aerosol product spatial distributions highlights the advantages of the proposed algorithm in capturing aerosol diurnal variations (such as haze dissipation processes) and extreme events (e.g., dust storms and biomass burning). This study provides a new technical approach for regional air quality monitoring and climate effect assessment, advancing the application of China’s geostationary meteorological satellites in aerosol monitoring.